{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(iris['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##independent features\n",
    "X=pd.DataFrame(iris['data'],columns=['sepal length in cm','sepal width','petal length','petal width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length in cm</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>145</th>\n",
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       "      <td>2.3</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length in cm  sepal width  petal length  petal width\n",
       "0                   5.1          3.5           1.4          0.2\n",
       "1                   4.9          3.0           1.4          0.2\n",
       "2                   4.7          3.2           1.3          0.2\n",
       "3                   4.6          3.1           1.5          0.2\n",
       "4                   5.0          3.6           1.4          0.2\n",
       "..                  ...          ...           ...          ...\n",
       "145                 6.7          3.0           5.2          2.3\n",
       "146                 6.3          2.5           5.0          1.9\n",
       "147                 6.5          3.0           5.2          2.0\n",
       "148                 6.2          3.4           5.4          2.3\n",
       "149                 5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##dependent features\n",
    "y=iris['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "##train test plot\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## apply Decision Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "treeclassifier=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treeclassifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Visualize the Decision Tree\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(treeclassifier,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=treeclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  0  0]\n",
      " [ 0 12  1]\n",
      " [ 0  0  7]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      0.92      0.96        13\n",
      "           2       0.88      1.00      0.93         7\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.96      0.97      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier Prepruning And Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "param={\n",
    "    'criterion':['gini','entropy', 'log_loss'],\n",
    "    'splitter':['best','random'],\n",
    "    'max_depth':[1,2,3,4,5],\n",
    "    'max_features':['auto','sqrt','log2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': ['gini', 'entropy', 'log_loss'],\n",
       " 'splitter': ['best', 'random'],\n",
       " 'max_depth': [1, 2, 3, 4, 5],\n",
       " 'max_features': ['auto', 'sqrt', 'log2']}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "treemodel=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid=GridSearchCV(treeclassifier,param_grid=param,cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
       "                         'max_depth': [1, 2, 3, 4, 5],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'splitter': 'random'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9583333333333333"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 1, 0, 1, 2, 1, 0, 1, 2, 2, 1, 0, 0, 2, 2, 0, 0, 0, 2, 2,\n",
       "       2, 0, 2, 0, 1, 1, 1, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  0  0]\n",
      " [ 0  9  4]\n",
      " [ 0  0  7]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        10\n",
      "           1       1.00      0.69      0.82        13\n",
      "           2       0.64      1.00      0.78         7\n",
      "\n",
      "    accuracy                           0.87        30\n",
      "   macro avg       0.88      0.90      0.87        30\n",
      "weighted avg       0.92      0.87      0.87        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "score=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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